子空间法雷达目标一维像识别研究

刘本永

子空间法雷达目标一维像识别研究

刘本永

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出版历程
- 收稿日期: 2003-01-21
- 修回日期: 2003-09-08
- 刊出日期: 2004-07-19

Subspace Methods of Radar Target Recognition Using Range Profiles

Liu Ben-yong

摘要

摘要: 基于高分辨一维像,研究特征子空间法和正则子空间法在雷达目标识别中的应用。针对一维像敏感于目标姿态变化的特点，提出一种子空间串识别法，将所有姿态范围划分为一定数量的模区，在每模区建立各类目标的子空间。对未知目标，所处模区由雷达测定后，其一维像映射到该模区各类目标的特征子空间进行识别分类一单模区搜索准则。模拟和实测数据实验表明所提出方法是有效的。
- 雷达目标一维像识别; 特征子空间; 正则子空间; 子空间串; 单模区搜索
Abstract: Eigen-subspace and canonical-subspace methods are studied and applied to feature-extraction for target recognition using range profiles of a High-range-Resolution-Radar (HRR) system. Based on this study, a subspace cluster method is proposed to tackle the problem of aspect-sensitivity of range profiles. In subspace cluster method, the aspect scope of a radar target is divided into a proper number of zones, and eigen-subspaces are established for each zone. After the zone number of an unknown target is determined by radar, the range profile of this target is mapped into eigen-subspaces of the corresponding zone, and the class whose subspace has the maximum mapping energy is judged as the right class to which the unknown target belongs. This method is named as single-mode classification rule in the subspace cluster method. Experimental results on simulated data and field data show the efficiency of the subspace methods and subspace cluster method in target recognition.

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Cohen M N. Variability of ultra-high range radar profiles and some implication for target recognition[J].Proc. SPIE.1992, Vol.1699:256-266[2]Li H J, Yang S H. Using range profiles as feature vectors to identify aerospace objects[J].IEEE Trans. on Antenna and Propagation.1993, 41(6):261-268[3]Smith C R, Goggans P M. Radar target identific ation[J].IEEE Antenna and Propagation Magazine.1993, 35(2):27-38[4]Hudson S, Psaltis D. Correlation filters for aircraft identification from radar range profiles[J].IEEE Trans. on Aerospace and Electronic System.1993, 29(3):741-748[5]郭桂蓉,庄钊文,陈曾平.电磁特征抽取与目标识别.长沙:国防科技大学出版社,1996:147-174.[6]奥亚E.著(芬兰),蔡国廉,杨文瑜译.子空间法模式识别.北京:科学出版社,1987:1-4.[7]McIlraith A L, Card H C. Birdsong recognition using backpropagation and multivariate statistics[J].IEEE Trans. on Signal Processing.1997, 45(11):2740-2748[8]Sirovich L, Kirby M. Low-dimensional procedure for the characterization of human faces[J].J. Opt.Soc. Am. A.1987, 4(3):519-524[9]Turk M A, Pentland A P. Face recognition using eigenfaces. Proc. IEEE Computer Soc. Conference on Computer Vision and Pattern Recognition, Hawaii, 1991: 586-591.[10]Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: Recognition using class specific linear-projection. IEEE Trans. on Pattern Analysis and Machine Intelligence,1997, 19(7): 711-720.[11]Singstock B D, Rogers S K, Kabrisky M, Ruck D W. Automatic target recognition using Karhunen-Loeve transform[J].Proc. SPIE.1992, Vol.1699:232-240[12]Novak L M, Owirka G J. Radar target identification using an eigen-image approach. IEEE Nat.Radar Conf., Atlanta, 1994: 129-131.[13]Biswas G, Jain A K, Dubes R C. Evaluation of projection algorithms[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.1981, 3(6):701-708[14]Siedlecki W, Siedlecka K, Sklansky J. An overview of mapping techniques for exploratory pattern analysis[J].Pattern Recognition.1988, 21(5):411-429[15]刘本水.杨万麟.噪声中目标距离剖面像识别的修正特征子空间方法.系统工程与电子技术,2000,22(3):30-32.[16]Hong Z Q, Yang J Y. Optimal discriminant plane for a small number of samples and design method of classifier on the plane[J].Pattern Recognition.1991, 24(4):317-324[17]Liu B Y, Yang W L. Radar target recognition using canonical transformation to extract features. Proc. SPIE, 1998, Vol.2545: 368-371.[18]刘本永,杨万麟.基于正则变换的雷达目标成像识别.系统工程与电子技术,1999,21(3):31-33,59.[19]刘本永,杨万麟.一种利用相位信息的雷达目标成像识别方法.电子科学学刊,2000,22(2),274-278.[20]Zhang X D, Shi Y, Bao Z. A new feature vector using selected bispectra for signal classification with application in radar target recognition[J].IEEE Trans. on Signal Processing.2001, 49(9):1875-1885[21]刘本永.子空间法雷达目标一维像识别研究.[博士论文],成都:电子科技大学,1999.

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